The Limitations of Optimization from Samples

In this paper we consider the following question: can we optimize objective functions from the training data we use to learn them? We formalize this question through a novel framework we call optimization from samples (OPS). In OPS, we are given sampled values of a function drawn from some distribution and the objective is to optimize the function under some constraint. While there are interesting classes of functions that can be optimized from samples, our main result is an impossibility. We show that there are classes of functions which are statistically learnable and optimizable, but for which no reasonable approximation for optimization from samples is achievable. In particular, our main result shows that there is no constant factor approximation for maximizing coverage functions under a cardinality constraint using polynomially-many samples drawn from any distribution. We also show tight approximation guarantees for maximization under a cardinality constraint of several interesting classes of functions including unit-demand, additive, and general monotone submodular functions, as well as a constant factor approximation for monotone submodular functions with bounded curvature

Information-Theoretic Bounds for Multiround Function Computation in Collocated Networks

We study the limits of communication efficiency for function computation in collocated networks within the framework of multi-terminal block source coding theory. With the goal of computing a desired function of sources at a sink, nodes interact with each other through a sequence of error-free, network-wide broadcasts of finite-rate messages. For any function of independent sources, we derive a computable characterization of the set of all feasible message coding rates - the rate region - in terms of single-letter information measures. We show that when computing symmetric functions of binary sources, the sink will inevitably learn certain additional information which is not demanded in computing the function. This conceptual understanding leads to new improved bounds for the minimum sum-rate. The new bounds are shown to be orderwise better than those based on cut-sets as the network scales. The scaling law of the minimum sum-rate is explored for different classes of symmetric functions and source parameters.Comment: 9 pages. A 5-page version without appendices was submitted to IEEE International Symposium on Information Theory (ISIT), 2009. This version contains complete proofs as appendice

Iterative model and trajectory refinement for orbital trajectory optimization

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139923/1/oca2319_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139923/2/oca2319.pd

Cosmological surveys with the Australian Square Kilometre Array Pathfinder

This is a design study into the capabilities of the Australian Square Kilometre Array Pathfinder in performing a full-sky low redshift neutral hydrogen survey, termed WALLABY, and the potential cosmological constraints one can attain from measurement of the galaxy power spectrum. We find that the full sky survey will likely attain 0.6 million redshifts which, when combined with expected Planck CMB data, will constrain the Dark Energy equation of state to 20%, representing a coming of age for radio observations in creating cosmological constraints.Comment: 10 pages, 6 figures, accepted in PASA, updated to match published versio

Evaluating Large Language Models on Controlled Generation Tasks

While recent studies have looked into the abilities of large language models in various benchmark tasks, including question generation, reading comprehension, multilingual and etc, there have been few studies looking into the controllability of large language models on generation tasks. We present an extensive analysis of various benchmarks including a sentence planning benchmark with different granularities. After comparing large language models against state-of-the-start finetuned smaller models, we present a spectrum showing large language models falling behind, are comparable, or exceed the ability of smaller models. We conclude that **large language models struggle at meeting fine-grained hard constraints**.Comment: EMNLP 202

Constant-Round Private Decision Tree Evaluation for Secret Shared Data

Decision tree evaluation is extensively used in machine learning to construct accurate classification models. Often in the cloud-assisted communication paradigm cloud servers execute remote evaluations of classification models using clients’ data. In this setting, the need for private decision tree evaluation (PDTE) has emerged to guarantee no leakage of information for the client’s input nor the service provider’s trained model i.e., decision tree. In this paper, we propose a private decision tree evaluation protocol based on the three-party replicated secret sharing (RSS) scheme. This enables us to securely classify inputs without any leakage of the provided input or the trained decision tree model. Our protocol only requires constant rounds of communication among servers, which is useful in a network with longer delays. Ma et al. (NDSS 2021) presented a lightweight PDTE protocol with sublinear communication cost with linear round complexity in the size of the input data. This protocol works well in the low latency network such as LAN while its total execution time is unfavourably increased in the WAN setting. In contrast, Tsuchida et al. (ProvSec 2020) constructed a constant round PDTE protocol at the cost of communication complexity, which works well in the WAN setting. Although their construction still requires 25 rounds, it showed a possible direction on how to make constant round PDTE protocols. Ji et al. (IEEE Transactions on Dependable and Secure Computing) presented a simplified PDTE with constant rounds using the function secret sharing (FSS) at the cost of communication complexity. Our proposed protocol only requires five rounds among the employed three servers executing secret sharing schemes, which is comparable to previously proposed protocols that are based on garbled circuits and homomorphic encryption. To further demonstrate the efficiency of our protocol, we evaluated it using real-world classification datasets. The evaluation results indicate that our protocol provides better concrete performance in the WAN setting that has a large network delay

Predictions for ASKAP Neutral Hydrogen Surveys

The Australian Square Kilometer Array Pathfinder (ASKAP) will revolutionise our knowledge of gas-rich galaxies in the Universe. Here we present predictions for two proposed extragalactic ASKAP neutral hydrogen (HI) emission-line surveys, based on semi-analytic models applied to cosmological N-body simulations. The ASKAP HI All-Sky Survey, known as WALLABY, is a shallow 3 Pi survey (z = 0 - 0.26) which will probe the mass and dynamics of over 600,000 galaxies. A much deeper small-area HI survey, called DINGO, aims to trace the evolution of HI from z = 0 - 0.43, a cosmological volume of 40 million Mpc^3, detecting potentially 100,000 galaxies. The high-sensitivity 30 antenna ASKAP core (diameter ~2 km) will provide an angular resolution of 30 arcsec (at z=0). Our simulations show that the majority of galaxies detected in WALLABY (87.5%) will be resolved. About 5000 galaxies will be well resolved, i.e. more than five beams (2.5 arcmin) across the major axis, enabling kinematic studies of their gaseous disks. This number would rise to 160,000 galaxies if all 36 ASKAP antennas could be used; the additional six antennas provide baselines up to 6 km, resulting in an angular resolution of 10 arcsec. For DINGO this increased resolution is highly desirable to minimise source confusion; reducing confusion rates from a maximum of 10% of sources at the survey edge to 3%. We estimate that the sources detected by WALLABY and DINGO will span four orders of magnitude in total halo mass (from 10^{11} to 10^{15} Msol) and nearly seven orders of magnitude in stellar mass (from 10^{5} to 10^{12} Msol), allowing us to investigate the process of galaxy formation across the last four billion years.Comment: 21 pages, accepted for publication in MNRAS, minor updates to published version and fixed links. Movies and images available at http://ict.icrar.org/store/Movies/Duffy12c

Effects of Soybean Agglutinin on Intestinal Barrier Permeability and Tight Junction Protein Expression in Weaned Piglets

This study was developed to provide further information on the intestinal barrier permeability and the tight junction protein expression in weaned piglets fed with different levels of soybean agglutinin (SBA). Twenty-five weaned crossbred barrows (Duroc × Landrace × Yorkshire) were selected and randomly allotted to five groups, each group with five replicates. The piglets in the control group were not fed with leguminous products. 0.05, 0.1, 0.15 and 0.2% SBA was added to the control diet to form four experimental diets, respectively. After the experimental period of 7 days (for each group), all the piglets were anesthetized with excess procaine and slaughtered. The d-lactic acid in plasma and the Ileal mucosa diamine oxidase (DAO) was analyzed to observe the change in the intestinal permeability. The tight junction proteins occludin and ZO-1 in the jejunum tissue distribution and relative expression were detected by immunohistochemistry and Western Blot. The results illustrated that a high dose of SBA (0.1–0.2%) could increase the intestinal permeability and reduce piglet intestinal epithelial tight junction protein occludin or ZO-1 expression, while low dose of SBA (0.05% of total diet) had no significant affects. The contents of DAO, d-lactic acid, occludin or ZO-1, had a linear relationship with the SBA levels (0–0.2%) in diets. The high dose SBA (0.1–0.2%) could increase the intestinal permeability and reduce piglet intestinal epithelial tight junction protein occludin or ZO-1 expression, while low dose of SBA (0.05% of total diet) had no affects

Differential diagnosis of breast cancer using quantitative, label-free and molecular vibrational imaging

We present a label-free, chemically-selective, quantitative imaging strategy to identify breast cancer and differentiate its subtypes using coherent anti-Stokes Raman scattering (CARS) microscopy. Human normal breast tissue, benign proliferative, as well as in situ and invasive carcinomas, were imaged ex vivo. Simply by visualizing cellular and tissue features appearing on CARS images, cancerous lesions can be readily separated from normal tissue and benign proliferative lesion. To further distinguish cancer subtypes, quantitative disease-related features, describing the geometry and distribution of cancer cell nuclei, were extracted and applied to a computerized classification system. The results show that in situ carcinoma was successfully distinguished from invasive carcinoma, while invasive ductal carcinoma (IDC) and invasive lobular carcinoma were also distinguished from each other. Furthermore, 80% of intermediate-grade IDC and 85% of high-grade IDC were correctly distinguished from each other. The proposed quantitative CARS imaging method has the potential to enable rapid diagnosis of breast cancer